Automatic annotation for vehicle damage

ABSTRACT

Aspects described herein may allow an automated generation of an interactive multimedia content with annotations showing vehicle damage. In one method, a server may receive vehicle-specific identifying information of a vehicle. Image sensors may capture multimedia content showing aspects associated with exterior regions of the vehicle, and may send the multimedia content to the server. For each of the exterior regions of the vehicle, the server may determine, using a trained classification model, instances of damage. Furthermore, the server may generate an interactive multimedia content that shows images with annotations indicating instances of damage. The interactive multimedia content may be displayed via a user interface.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.17/089,881 filed on filed on Nov. 5, 2020, entitled “AutomaticAnnotation for Vehicle Damage,” which is a continuation of U.S.application Ser. No. 16/786,695 filed on Feb. 10, 2020, and entitled“Automatic Annotation for Vehicle Damage,” which issued as U.S. Pat. No.10,846,322 on Nov. 24, 2020, each of which is hereby incorporated byreference as to its entirety.

FIELD OF USE

Aspects of the disclosure relate generally to electronic devices. Morespecifically, aspects of the disclosure may provide for systems andmethods for automatic annotation for vehicle damage.

BACKGROUND

Auto dealerships today may typically post photos of vehicles online forsale, rent, or other leasing arrangements. When potential buyers,renters, or lessors (collectively referred to as “users”) are viewinginventory of these vehicles online, it may be difficult for the users toidentify existing damages on or within the vehicles. If the userundertakes the burden to physically drive to the location of the vehiclewith the intent of the buying, renting, or leasing the vehicle, seeingexisting damage (often for the first time) on the vehicle may create anegative experience for the user. There is thus a desire for a reliablemeans for identifying and presenting existing damage of vehicles.Systems and methods are presented to overcome one or more of theshortcomings presented above.

SUMMARY

The following presents a simplified summary of various aspects describedherein. This summary is not an extensive overview, and is not intendedto identify key or critical elements or to delineate the scope of theclaims. The following summary merely presents some concepts in asimplified form as an introductory prelude to the more detaileddescription provided below.

Aspects described herein may allow a user to view an interactivemultimedia content online of a vehicle for sale, rent, or other leasingarrangement. The interactive multimedia content may have annotations toindicate instances of damage to the vehicle, thereby informing the userin advance and enhancing the online experience of the user.

These and other aspects of the present disclosure may provide variousbenefits to the auto dealers, prospective customers, and the vehicleindustry. For example, systems and methods that facilitate a user totest drive a desired vehicle without an employee to be present may allowauto dealers to cut costs and unnecessary time and labor. Furthermore,these systems and methods may enhance a user's experience by allowingthe user to directly test drive the desired vehicle without having todeal with constraints or restrictions presented by an auto dealer. Evenfurther, these systems and methods may provide automatic tracking ofvehicle test drives, thereby assuring safety.

According to some aspects, these and other benefits may be achieved by astaging area with associated instruments and devices (e.g., a stagingarea system) to capture multimedia content of a vehicle. A computingsystem or server located remotely or locally to the staging area systemmay facilitate automatic annotation for vehicle damage with limitedhuman interaction. For example, in at least one implementation, theserver may receive vehicle-specific identifying information of avehicle. Image sensors at the staging area system may capture multimediacontent showing aspects associated with exterior regions of the vehicle,and may send the multimedia content to the server. For each of theexterior regions of the vehicle, the server may determine, using atrained classification model, instances of damage. Furthermore, theserver may generate an interactive multimedia content that shows imageswith annotations indicating instances of damage. The interactivemultimedia content may be displayed via a user interface and responsiveto a request.

Corresponding apparatus, systems, and computer-readable media are alsowithin the scope of the disclosure.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIG. 1 depicts an example of a computing device that may be used inimplementing one or more aspects of the disclosure in accordance withone or more illustrative aspects discussed herein;

FIG. 2 depicts an example environment in accordance with one or moreillustrative aspects discussed herein;

FIG. 3 depicts an example network in accordance with one or moreillustrative aspects discussed herein;

FIG. 4 depicts a flow diagram of an example method for automaticallyannotating vehicle damage and generating an interactive multimediacontent, in accordance with one or more illustrative aspects discussedherein;

FIG. 5 depicts a flow diagram of an example method for facilitatingautomatic annotation for vehicle damage, in accordance with one or moreillustrative aspects discussed herein;

FIG. 6 depicts a flow diagram of an example method for training andapplying a classification model to determine one or more instances ofdamage to a vehicle, in accordance with one or more illustrative aspectsdiscussed herein; and

FIG. 7 depicts a flow diagram of an example method for training andapplying a machine learning model to identify one or more aspects of avehicle, in accordance with one or more illustrative aspects discussedherein.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration various embodiments in whichaspects of the disclosure may be practiced. It is to be understood thatother embodiments may be utilized and structural and functionalmodifications may be made without departing from the scope of thepresent disclosure. Aspects of the disclosure are capable of otherembodiments and of being practiced or being carried out in various ways.Also, it is to be understood that the phraseology and terminology usedherein are for the purpose of description and should not be regarded aslimiting. Rather, the phrases and terms used herein are to be giventheir broadest interpretation and meaning. The use of “including” and“comprising” and variations thereof is meant to encompass the itemslisted thereafter and equivalents thereof as well as additional itemsand equivalents thereof.

By way of introduction, aspects discussed herein may relate to systems,methods, techniques, apparatuses, and non-transitory computer readablemedia for automatic annotation for vehicle damage. For example,information about a vehicle for sale, rent, or other leasing arrangementmay be presented online and/or via a mobile application for a user toaccess via a user device. Instruments (e.g., image sensors, cameras,etc.) at a staging area may initially (e.g., before the vehicle ispresented for the sale, rent, or other leasing arrangement) and/orperiodically generate multimedia content (e.g., images) of the vehicle.A server, located locally or remotely to the staging area, may assessthe generated multimedia content for any indications of damage to thevehicle. The server may generate an interactive multimedia content ofthe vehicle, with annotations for any indications of damage to thevehicle, for the user to access via the user device. An interactivemultimedia content may comprise, for example, a set of multimediacontent (e.g., one or more images, one or more videos, etc.) that may becapable of being adjusted and/or modified via user input. For example,an interactive multimedia content may include a 360 degree image of avehicle, and a user may be able to zoom in, zoom out, and/or viewvarious perspectives of the 360 degree image by rotating the image viauser input. The interactive multimedia content generated by the servermay rely on multimedia content generated by instruments at the stagingarea but the interactive multimedia content may nevertheless bedistinguishable from the multimedia content. For example, theinteractive multimedia content may be based on an analysis of themultimedia content and may include annotations to indicate instances ofdamage to the vehicle. The server, user device, vehicle systems, and/orany devices at the staging area may be examples of one or more computingdevices. As discussed further herein, this combination of features mayfacilitate an automatic annotation for vehicle damage.

Before discussing these concepts in greater detail, however, severalexamples of a computing device that may be used in implementing and/orotherwise providing various aspects of the disclosure will first bediscussed with respect to FIG. 1 .

FIG. 1 illustrates one example of a computing device 101 that may beused to implement one or more illustrative aspects discussed herein. Forexample, computing device 101 may, in some embodiments, implement one ormore aspects of the disclosure by reading and/or executing instructionsand performing one or more actions based on the instructions. In someembodiments, computing device 101 may represent, be incorporated in,and/or include various devices such as a desktop computer, a computerserver, a mobile device (e.g., a laptop computer, a tablet computer, asmart phone, any other types of mobile computing devices, and the like),and/or any other type of data processing device. Furthermore, whilecomputing device 101 is shown to be separate from devices 105, 107, and109, one or more components and one or more functions of computingdevice 101 may also be attributed to devices 105, 107, and 109.

Computing device 101 may, in some embodiments, operate in a standaloneenvironment. In others, computing device 101 may operate in a networkedenvironment. As shown in FIG. 1 , various network nodes 101, 105, 107,and 109 may be interconnected via a network 103, such as the Internet.The various network nodes may include for example, a user device 109associated with a user that views an interactive multimedia content of avehicle, a computing device associated with a staging area 104 (“stagingarea system” 105), where multimedia content of the vehicle is generatedvia instruments 106 and an example computing device 101 that mayrepresent a server that performs an automatic annotation for vehicledamage, and which may include components and functions shared by theother devices. Furthermore, the vehicle may comprise a device 107 (e.g.,a telematics system) that may be used in one or more aspects describedherein. Other networks may also or alternatively be used, includingprivate intranets, corporate networks, LANs, wireless networks, personalarea networks (PAN), and the like. Communications network 103 is forillustration purposes and may be replaced with fewer or additionalcomputer networks. A local area network (LAN) may have one or more ofany known LAN topology and may use one or more of a variety of differentprotocols, such as Ethernet. Devices 101, 105, 107, 109 and otherdevices (not shown) may be connected to one or more of the networks viatwisted pair wires, coaxial cable, fiber optics, radio waves or othercommunication media.

As seen in FIG. 1 , computing device 101 may include a processor 111,RAM 113, ROM 115, network interface 117, input/output interfaces 119(e.g., keyboard, mouse, display, printer, etc.), and memory 121.Processor 111 may include one or more computer processing units (CPUs),graphical processing units (GPUs), and/or other processing units such asa processor adapted to perform computations assessing multimedia contentassociated with a vehicle, determining instances of damage to one ormore aspects of the vehicle, generating interactive multimedia contentwith annotations showing the instances of damage, and other functions.I/O 119 may include a variety of interface units and drives for reading,writing, displaying, and/or printing data or files. I/O 119 may becoupled with a display such as display 120. Memory 121 may storesoftware for configuring computing device 101 into a special purposecomputing device in order to perform one or more of the variousfunctions discussed herein. Memory 121 may store operating systemsoftware 123 for controlling overall operation of computing device 101,control logic 125 for instructing computing device 101 to performaspects discussed herein. Furthermore, memory 121 may store variousdatabases and applications depending on the particular use. For example,if the computing device 101 is a server used for the automaticannotation for vehicle damage, the memory 121 may include a database forvehicle profiles (e.g., “vehicle profiles” 127), and a repository ofmultimedia content generated for a plurality of vehicles (e.g.,“multimedia content repository” 133). Vehicle profiles 127 may storeinformation and tools that are specific to a vehicle profile (e.g., acategory of vehicles based on their manufacture, model, class, vehicletype, etc.). These tools may include, for example, trained predictionmodels 131 (e.g., trained machine learning algorithms, trainedclassification models, etc.) that have been trained based on referencetraining data (e.g., multimedia content of a plurality of referencevehicles). For example, a trained prediction model may identify anaspect of a vehicle belonging to a vehicle profile using multimediacontent associated with the vehicle, and another trained predictionmodel may determine instances of damage to one or more aspects of avehicle belonging to a vehicle profile using multimedia contentassociated with the vehicle. The vehicle profiles 127, trainedprediction models 131, and multimedia content repository 133 are shownin dotted lines to indicate its specific relevance to a computing device101 of a server for automatic annotation for vehicle damage.

One or more applications and/or application program interfaces (API) 135may also be stored in the memory of the computing device 101. Forexample, if the computing device 101 is a user device 109 associatedwith a user, the computing device 101 may include an application thatallows the user to browse vehicles for sale, rent, or other leasingarrangements, and view their interactive multimedia content. The usermay browse vehicles through this application, and/or may search forvehicles belonging to various vehicle profiles. If the computing device101 is a server performing an automatic annotation for vehicle damage,the computing device 101 may include APIs for generating the interactivemultimedia content and assessing damages.

Control logic 125 may be incorporated in and/or may comprise a linkingengine that updates, receives, and/or associates various informationstored in the memory 121 (e.g., vehicle-specific identifyinginformation, multimedia content, trained models for identifying aspectsof a vehicle belonging to a vehicle profile, trained models fordetermining instances of damage for one or more aspects of a vehiclebelonging to a vehicle profile etc.). In other embodiments, computingdevice 101 may include two or more of any and/or all of these components(e.g., two or more processors, two or more memories, etc.) and/or othercomponents and/or subsystems not illustrated here.

Devices 105, 107, 109 may have similar or different architecture asdescribed with respect to computing device 101. Those of skill in theart will appreciate that the functionality of computing device 101 (ordevice 105, 107, 109) as described herein may be spread across multipledata processing devices, for example, to distribute processing loadacross multiple computers, to segregate transactions based on geographiclocation, user access level, quality of service (QoS), etc. For example,devices 101, 105, 107, 109, and others may operate in concert to provideparallel computing features in support of the operation of control logic125 and/or software 127.

One or more aspects discussed herein may be embodied in computer-usableor readable data and/or computer-executable instructions, such as in oneor more program modules, executed by one or more computers or otherdevices as described herein. Generally, program modules includeroutines, programs, objects, components, data structures, etc. thatperform particular tasks or implement particular abstract data typeswhen executed by a processor in a computer or other device. The modulesmay be written in a source code programming language that issubsequently compiled for execution, or may be written in a scriptinglanguage such as (but not limited to) HTML or XML. The computerexecutable instructions may be stored on a computer readable medium suchas a hard disk, optical disk, removable storage media, solid statememory, RAM, etc. As will be appreciated by one of skill in the art, thefunctionality of the program modules may be combined or distributed asdesired in various embodiments. In addition, the functionality may beembodied in whole or in part in firmware or hardware equivalents such asintegrated circuits, field programmable gate arrays (FPGA), and thelike. Particular data structures may be used to more effectivelyimplement one or more aspects discussed herein, and such data structuresare contemplated within the scope of computer executable instructionsand computer-usable data described herein. Various aspects discussedherein may be embodied as a method, a computing device, a dataprocessing system, or a computer program product.

Having discussed several examples of computing devices which may be usedto implement some aspects as discussed further below, discussion willnow turn to an illustrative environment and network for test drivingvehicles with minimal human interaction.

FIG. 2 depicts an example environment 200 in accordance with one or moreillustrative aspects discussed herein. In at least one aspect, a user202, via a mobile device 204, may be able to access and interact with aninteractive multimedia content associated with a vehicle 210 and showinginstances of damage to the vehicle. The interactive multimedia content,and the annotations of damage, may be generated by a server 201 withminimal human interaction. For example, an automobile dealership orother vendor may keep vehicles for sale, rent, or other purchasearrangements, and may advertise or promote these vehicles via an onlinemarketplace, where interactive multimedia content associated with thevehicles may assist users in the online marketplace who are interestedin knowing more about the vehicles.

The annotations to indicate instances of damage to the vehicle may begenerated by determining the instances of damage via an examination ofthe vehicle. For example, vehicles, such as vehicle 206, may beroutinely examined for damages (e.g., at the time of a test drive, sale,rent, or other purchase agreement). Thus, a vehicle being returned to anautomobile dealership after a test-drive or short term rental may beexamined for damages before it is placed in the market again. Also oralternatively, vehicles may be examined for damages when they are beingonboarded for a trade-in, e.g., at a dealership. The onboarding of avehicle may be performed to document damages, e.g., for a vehiclelisting.

The vehicle 206 may be examined by being brought into a staging area212. In various embodiments, a “staging area” may be used to refer to apredetermined area for the placement of a vehicle where instrumentsassociated with the staging area can automatically generate multimediacontent concerning various aspects of the vehicle. For example, as shownin FIG. 2 , staging area 212 is a predetermined area for a vehicle to beplaced so that various instruments (e.g., camera 224, sound recorder228, etc.) can be better poised at obtaining data (e.g., photos, videos,recordings, etc.) of the vehicle 206. This generated multimedia contentmay be delivered to a server 201 over a communications network. Forexample, a communications module 230 near the staging area 212 mayfacilitate communication between the server 201 and the variousinstruments associated with the staging area 212. A “staging areasystem” may refer to the various instruments, devices, and/or computingsystems associated with a staging area, collectively, or to a centraldevice or computing system 218 that receives data obtained from thevarious instruments (e.g., as in staging area system 105 in FIG. 1 ). Insome aspects the staging area system 218 may detect the placement orpresence of a vehicle 206 via a vehicle detector 214 (e.g., motiondetector, heat sensor, image sensor, etc.). Through this detection, thestaging area system may deliver a feedback to the server 201 that avehicle has entered the staging area 214. The instruments 224 and 228may generate multimedia content (e.g., photos, videos, sound recordings,etc.) of the incoming or placed vehicle 206 and the multimedia contentcan be used to authenticate the vehicle. Furthermore, the instruments224 and 228 may be used to generate further multimedia content of one ormore aspects of the vehicle 206. In some implementations, theinstruments 224 and 228, and/or sensor 214 may be aided by illuminationprovided by light sources (e.g., lamp 222). The light sources may beperiodically adjusted or calibrated based on the level of sunlight 236.Furthermore, the staging area may be covered 232, e.g., to maintainconsistency and reliability of generated multimedia content. In someaspects, the server 201 may also receive data that could indicate adamage to the vehicle 206 from a telematics system 210 of the vehicle206. In some aspects, the vehicle system 107 described in relation toFIG. 1 may comprise the telematics system 210 of the vehicle 206 shownin FIG. 2 . The data received from the telematics system 210 may bevalues (e.g., readings from one or more sensors within the vehicle)and/or may be presented as a form of multimedia content. For example, adata received from the telematics system may indicate that an oil filteris at 5%, or that a rear tire of the vehicle is only 30% inflated.

The server 201 may be a computing device or system located locally orremotely from the staging area 212 and may comprise one or morecomponents of, and perform one or more functions described for,computing device 101 in FIG. 1 . As will be described further below, inrelation to FIGS. 4, 6 and 7 , the server 201 may receive multimediacontent generated by the staging area system 218 and/or the telematicssystem 210. The server 201 may utilize trained models to identifyvarious aspects of the vehicle 206 and determine any instances ofdamage. The server 201 may generate an interactive multimedia contentwith annotations to indicate the instances of damage. A user 202, whomay be located remotely from the vehicle 206, may access and interactwith the interactive multimedia content via his or her user device 204(e.g., as in user device 109 of FIG. 1 ). The user 202 may use theinteractive multimedia content to learn more about vehicle 206, e.g., tomake a decision of whether or not to purchase the vehicle for sale,rent, or other leasing arrangement. The interactive multimedia contentmay enhance the user's experience in purchasing, renting, or leasing thevehicle 206, by being made aware of damages to the vehicle in a reliableand accurate manner.

FIG. 3 depicts an example network 300 in accordance with one or moreillustrative aspects discussed herein. Each component or subcomponentshown in FIG. 3 may be implemented in hardware, software, or acombination of the two. Additionally, each component or subcomponent mayinclude a computing device (or system) having some or all of thestructural components described above for computing device 101. At ahigh level, the network 300 may include, for example, one or more userdevices (e.g., user device 302) associated with different users, one ormore staging area systems (e.g., staging area system 320), one or morevehicle systems (e.g., vehicle system 334), and one or more serversystems (e.g., server system 352). The user device 302 may comprise amobile phone (e.g., a smartphone), personal computer, tablet computer,laptop, or the like, which may include at least some of the featuresdescribed herein. The user device 302 may belong to a user that accessesan online market for the sale, rent, or other leasing arrangement ofvehicles, and may access and interact with interactive multimediacontent associated with vehicles. The interactive multimedia content maypresent visual information (e.g., images, 3-D scan, etc.) pertaining toa vehicle and may be annotated to indicate any instances of damage. Theannotations to indicate damage, and the generation of the interactivemultimedia content may be performed by the server system 350.Furthermore, the server system 350 may generate the interactivemultimedia content of a vehicle and may generate annotations for damageto the vehicle based on multimedia content of the vehicle that theserver system 350 received from the staging area system 320, and may bebased on data received from the vehicle system 334. The staging areasystem 320 may generate the multimedia content of the vehicle and sendthe generated multimedia content to the server system 350 over acommunications network 380. Furthermore, the vehicle system 334 mayreceive data pertaining to the vehicle (e.g., via various telematicssensors 338), and send the data to the server system 350 over thecommunications network 380. The server system 350 may use the receivedmultimedia content and/or data to determine any instances of damage toone or more aspects of the vehicle, and generate an interactivemultimedia content of the vehicle with annotations to indicate anydamage to the vehicle.

According to some aspects of the disclosure described herein, the userdevice 302 may comprise one or more components or features describedbelow. The user device 302 may be an example of device 109 shown in FIG.1 , and/or an example of user device 204. The user device may be able toform wired and/or wireless data connections with one or more componentsof the server system 350 (e.g., as described further below) via aninternet and/or other telecommunications network (e.g., communicationsnetwork 380), using network interface 308. The user device 302 mayinclude a global positioning system (GPS) 304 (or other location sensor)that could be used, e.g., by other systems, to track the location of theuser device 302 and recommend nearby vehicles for sale, rent, or otherleasing arrangements on the online marketplace. The user device may alsocomprise a memory 306 and a processor 310 that may perform functionssimilar to memory 121 and processor 111 of computing device 101 shown inFIG. 1 . For example, the memory 306 of the user device 302 may storeuser-specific identifying information and financial information that canbe accessed by or sent to the server system 350, e.g., as metadata. Insome aspects, the memory store and run various aspects of theapplication 314.

The user interface 314 may be a display coupled with input devices(e.g., keyboard, type pad, touch screen, mouse, buttons, icons,microphone, sensors, etc.) that allows a user to access and browselistings of vehicles for sale, rent, or other lease arrangement on anonline marketplace. As used herein, an online marketplace may refer toany type of electronic platform (e.g., an e-commerce site) whereproducts (e.g., vehicles) and/or services (e.g., rental agreements,test-drive agreements, etc.) are promoted, advertised, and/or oroffered. In some aspects, the online marketplace may facilitate afinancial transaction for a user to procure a promoted, advertised,and/or offered product and/or service. The online marketplace may behosted by the server system 350 and/or by an external system that iscommunicatively coupled to the server system 350. In some aspects, theonline marketplace may be accessible to the user via an application 314on the user device. The user interface 312 may further allow the user toaccess, view, and interact with an interactive multimedia content of adesired vehicle (e.g., interactive multimedia content 318), includingannotations that indicate any instances of damage to one or more aspectsof the desired vehicle. Each vehicle listed in the online marketplacemay have its own interactive multimedia content generated according tomethods presented herein. As discussed previously, an interactivemultimedia content may comprise, for example, a set of multimediacontent (e.g., one or more images, one or more videos, etc.) that may becapable of being adjusted and/or modified via user input. For example,an interactive multimedia content may include a 360 degree image of avehicle, and a user may be able to zoom in, zoom out, and/or viewvarious perspectives of the 360 degree image by rotating the image viauser input.

The user device 302 may also run programs or applications 314, which maybe stored in the memory 306, and which may be accessed via the userinterface 314. One application or program may enable a user to use theonline marketplace for browsing vehicles listed for sale, rent, or otherleasing arrangements. A user may bookmark or otherwise indicatepreferences for a vehicle profile when browsing or searching vehicles.The bookmarked vehicle profiles or preferences for the vehicle profilesmay be saved, e.g., as stored vehicle profiles 316. In variousembodiments, a vehicle profile may be a category of vehicles based ontheir manufacture, model, class, vehicle type, etc. The same or anotherapplication may allow the user to view and interact with an interactivemultimedia content of a desired vehicle (e.g., interactive multimediacontent 318). The interactive multimedia content 318 of various vehiclesmay be downloaded and/or saved to the user device and may includeannotations that indicate any instances of damage to one or more aspectsof the vehicle. The above described applications 314 or programs may beprovided to the user device or hosted by the server system 350 (e.g.,via an application program interface (API) 368).

The staging area system 320 may include one or more devices,instruments, and/or sensors at, adjacent to, or associated with astaging area of a vehicle. The staging area system 320 may include oneor more of the features and components described below, and may be anexample of staging area system 105 shown in FIG. 1 and staging areasystem 218 shown in FIG. 2 . The staging area system 320 may includevarious instruments, sensors and devices configured to: generatemultimedia content that capture physical data of the vehicle parked inthe staging area at, adjacent to, or associated with the staging areasystem 318; track the entry, exit, and presence of vehicles (e.g., viavehicle detector 332); calibrate or adjust the conditions for thegeneration of multimedia content (e.g., via illumination devices 330that may calibrate based on sunlight), and/or transmit sensor data tothe server system 350 over the communications network 380. For example,the staging area system 320 may include instruments 322 that may includebuilt-in or affixed cameras and/or image sensors 324. The cameras and/orimage sensors 324 may be used to generate images, videos, and/or audiovisual content of an aspect of the vehicle (e.g., an exterior orinterior region of the vehicle). The cameras and/or image sensors 324may be placed at various angles facing the staging area or vehicle, orat various locations near or at the staging area, e.g., to capturedifferent aspects of the vehicle. For example, a camera at the base(e.g., ground) of the staging area may be used to generate multimediacontent that could show whether the vehicle has any leakage issues. Inanother example, a camera hovering above the staging area may be used togenerate image and/or video content that reveal any issues to the topexterior of the vehicle. The instruments 322 may further include soundrecorders 326 to generate audio content of an aspect of the vehicle(e.g., engine performance, engine issues, cable issues, etc.). The soundrecorders 326 may be able to capture sound recordings of a vehicle whilethe vehicle is kept running or an operator of the vehicle is performinga prescribed function. The images, videos, and/or audio generated by thevarious instruments 322 may be collectively referred to as “multimediacontent” for simplicity. The multimedia content generated from theinstruments 322 may be sent to the server system 350 over thecommunications network 380. However, the multimedia content generated bythe various instruments 322 and sent to the server system 350 may bedistinguishable from the interactive multimedia content generated by theserver system 350. For example, the interactive multimedia content mayinclude annotations to indicate one or more instances of damage to avehicle, and these damages may be based on the conditions of variousaspects of the vehicle.

As used herein, an aspect of a vehicle may refer to one or more of: anexterior or interior region, an electrical and/or mechanical componentof the vehicle, or a performance of the vehicle. An aspect of a vehiclemay be susceptible to damage (e.g., dents, leakages, wear and tear,etc.). A condition or state of an aspect of a vehicle may be ascertainedthrough a multimedia content (e.g., image, audio, video, etc.) capturingphysical data pertaining to the aspect. Thus, physical information(e.g., images, sounds, etc.) captured from the multimedia content mayindicate a condition of an aspect of the vehicle and may be analyzed bythe server system 350 to detect any damage to the vehicle. In someaspects, the instruments 322 may generate and send digital data (e.g.,image data) that the server system 350 may use to process and/or createinteractive multimedia content. The staging area system 318 may comprisea network interface 328 to establish wireless, wired, or networkconnections with one or more other systems (e.g., the server system 350,the vehicle systems 334, etc.)

The vehicle system 334 may include one or more devices, computingsystems, circuitry or sensors that are interior to, exterior to, orotherwise associated with a vehicle. For example, the vehicle system 334may include a telematics system 210, as shown in FIG. 2 . The vehiclesystem 334 may include one or more of the features and componentsdescribed below, according to some aspects of the present disclosure,and may be an example vehicle system 107 shown in FIG. 1 and vehiclesystem 210 shown in FIG. 2 . For example, the vehicle system 334 mayinclude various sensors configured to capture a condition of an aspectof the vehicle (e.g., telematics sensors 338) or collect locational orgeographical information (e.g., GPS device 340). The telematics sensors338 may compile the data captured by various sensors measuring orassessing the performance of various aspects of the vehicle. Thelocation sensor (e.g., a global positioning service (GPS)) 340 maycapture and present a location of the vehicle. The location of thevehicle 334 may be used by the server system 350, e.g., in order to listvehicles close to a user device of a user viewing the online marketplaceof vehicles for sale, rent, or other leasing arrangements. Thetelematics sensors 340 may further include, but are not limited to, anoil filter sensor, an odometer, a fuel tank sensor, a thermometer, avehicle computer, or a voltage sensor. Vehicle computers may be accessedthrough an OBD2 port, Bluetooth capabilities, or the like. The vehiclecomputer may be used to access additional sensors (e.g., tire pressuremonitoring systems) or vehicle information (e.g., engine codes). Theserver system 350 may use the readings or measurements obtained fromthese and other telematics sensors to detect damages or issues to thevehicle, and generate annotations to indicate such damages or issues inthe generated interactive multimedia content of the vehicle. The vehiclesystem 334 may send information to or receive information from othersystems (e.g., the mobile device 302, the staging area system 320, theserver system 350, etc.) over the communications network 390, vianetwork interface 336.

The server system 350 may comprise one or more remote, local, and/orconnected computing systems or servers managing one or more functions ofthe above-described systems (e.g., the mobile device 302, the stagingarea system 320, the vehicle system 334, etc.) to facilitate methods andsystems described herein. The server 350 may be connected to the stagingarea system 320 and/or may be located remotely from the staging areasystem 320. The server system 350 may be an example of computing device101 shown in FIG. 1 and/or server 201 shown in FIG. 2 . At a high level,the server system may comprise one or more databases 352, applicationprogram interfaces (APIs) 368, machine learning tools 364, a linkingengine 366, an update interface 372, and a network interface 374. Theupdate interface 372 and linking engine 366 may form a databasemanagement application, software, or plug-in that may be used to performcreate, read, update, or destroy (CRUD) functions with respect to datastored in the one or more databases 352. For example, the linking engine366 may be used to form associations or link suitable data fromdifferent databases together, and/or to create new data based onassociations or linkages. The update interface 372 may be used to updatedatabases (e.g., by adding or deleting) data stored in the one or moredatabases 352 based on instructions from other parts of the serversystem 350 (e.g., computer readable instructions stored in memory of anAPI) or information received from one or more external systems (e.g.,the mobile device 302, the staging area system 320, the vehicle system334, etc.). The server system 350 may send information to or receiveinformation from the external systems over the communications network390 via the network interface 374.

The server system 350 may include one or more databases described below.For example, the server system 350 may include a user profiles database354, which store identifying or biographical information pertaining to auser (e.g., user profiles) and/or link the user to a user device. A userprofile may be based on, or associated with, an identifier of the userdevice 302 of the user. The user profiles database 354 may store userpreferences, favorites, and/or bookmarked listings of vehicles and/orvehicle profiles in the online marketplace. In some aspects, the userprofiles database 354 may store financial information of the userintended to be used in transactions involving vehicles in the onlinemarketplace.

The server system 350 may include a vehicle profiles database 358 forstoring vehicle profiles. A vehicle profile may identify individualvehicles, e.g., by vehicle identification numbers, license platenumbers, and/or or other vehicle descriptors, based on a category ofvehicles in which the individual vehicle falls under. The vehicleprofiles database 358 may store information pertaining to one or more ofa make, model, class, year of manufacture, color, type, or category of avehicle. For example, the vehicle profiles database 358 may identifyvehicles that online marketplace advertises, promotes, and/or offers forsale, rental, and/or other leasing arrangement.

As will be described further below, the vehicle profiles database 358may also store programs and other tools that are specific to a categoryof vehicles (e.g., a vehicle profile). These programs may include, forexample, trained models that identify an aspect of a vehicle (e.g.,trained models for aspect identification 360). The trained models foraspect identification may identify an aspect (e.g., a region or part) ofa vehicle from a multimedia content (e.g., an image) of the vehicle,based on the vehicle profile of the vehicle. Thus, the trained modelcould identify an image of a front of a 2019 TOYOTA COROLLA LE as beingan image of the front exterior of the vehicle. The stored programs mayalso include trained models for detecting damage to one or more aspectsof a vehicle (e.g., trained models for damage detection 362). Thetrained model may be specific to an aspect of the vehicle, and maydetect damage from a multimedia content of the aspect of the vehicle.The trained models may be trained machine learning and/or classificationmodels that have been trained based on reference data obtained, e.g.,from externals systems or the multimedia content repository 356.Furthermore, ML tools 364 may be an application, program, or softwareused to train the described models from the reference data.

The sever system 352 may include a database for multimedia contentcollected from a plurality of vehicles (e.g., multimedia contentrepository 356). The multimedia content stored in the multimedia contentrepository 356 may include metadata that may reveal the time, date,and/or geographic location of the generation of the multimedia content.The multimedia content repository 356 may also identify a vehicle orvehicle profile to which a stored multimedia content pertains to, andmay indicate the aspect of the vehicle that the multimedia contentpurports to represent. Furthermore, the multimedia content repository356 may store multimedia content pertaining to a plurality of referencevehicles of a plurality of vehicle profiles, e.g., to train machinelearning algorithms, classification models, and other learned orprediction models. The linking engine 366 may associate multimediacontent stored in the multimedia content repository 356 to an individualvehicle and/or a vehicle profile indexed, for example, in the vehicleprofiles database 358.

The server system 350 may include one or more APIs (e.g., as describedbelow). The server system 350 may include, e.g., an API for anapplication for generating an interactive multimedia content (e.g.,interactive multimedia content generator 370). The server system 350 mayinclude an API for detecting instances of damage to a vehicle andperforming annotations to the interactive multimedia content based onthe detected instances of damage (e.g., damage detection 371). Theinteractive multimedia content and the annotations for any instances ofdamage to the vehicle may be accessible to a user browsing the onlinemarketplace via an application (e.g., application 314 on user device302).

The above described network interfaces (e.g., network interface 308,network interface 328, network interface 336, and network interface 374)may comprise a wireless communications interface, such as a cellularconnection (e.g., LTE, 5G), a Wi-Fi connection (e.g., Wi-Fi 5 or Wi-Fi6). Furthermore, the above described network interfaces may be examplesof network interface 117 shown in FIG. 1 .

In some implementations, server system 350 may comprise more than oneserver delegated to perform different functions. For example, one servermay manage financial transactions over the online marketplace ofvehicles for sale, rent, or other leasing arrangements, whereas anotherserver may generate interactive multimedia content of a vehicle withannotations to indicate instances of damage to a vehicle. Furthermore,one server may manage applications for viewing and interacting with theinteractive multimedia content on the online marketplace, e.g., via theapplication program interface 368.

In some implementations, the example network 300 shown in FIG. 3 mayreceive data from external system(s) (e.g., of municipal offices, policedepartments, and/or government). For example, the server system 350 mayreceive accident reports of vehicles from the computing systems ofmunicipal offices. These accident reports may include damage reports,describing, e.g., where damage may have occurred. The example network300 may thus include the external system(s) 380. Each of the externalsystem(s) 380 may also be accessible to one or more of the systemsdescribed previously over the communications network 380 via a networkinterface 382. The accident reports (e.g., accident reports 384) may beaccessed from the external system(s) 380 and may be used by the API 368of server system 350 to determine instances of damage to one or moreaspects of a vehicle, and generate interactive multimedia content withannotations to reflect the instances of damage. For example, rather thanjust recognizing new or not previously captured instances of damage viaroutine inspection, the server system 350 may determine (e.g., afterprocessing, parsing, and/or analyzing an accident report) areas of avehicle where damage has been reported in the accident report. Theserver system 350 may instruct the staging area system 320 to capturemultimedia content of those areas. The captured multimedia content maybe used to illustrate the extent of the damage reported in the accidentreport or to determine that there may be a lack of visible damage, e.g.,in order to improve customer expectations.

FIG. 4 depicts a flow diagram of an example method 400 for automaticallyannotating vehicle damage and generating an interactive multimediacontent, in accordance with one or more illustrative aspects discussedherein. Method 400 may be performed by one or more components (e.g.,processors) of server system 350 shown in FIG. 3 , server 201 shown inFIG. 2 , and/or computing device 101 shown in FIG. 1 . For convenience,“server” may be used herein to identify the performer of one or moresteps of method 400. As explained previously, a vehicle may be examinedfor instances of damage. The examination may be performed periodically(e.g., while the vehicle is advertised, promoted, or offered for sale,rent, or other leasing arrangement) or non-periodically (e.g.,initially, before the vehicle is advertised, promoted, and/or offeredfor sale, rent, or other leasing arrangement). The examination may occurwhen a vehicle is brought to a staging area, where instruments, devices,and systems of a staging area system proceed to generate multimediacontent. However, the generated multimedia content can be analyzed bythe server to determine instances of damages to one or more aspects ofthe vehicle.

The server may begin operations of automatically annotating vehicledamage and generating an interactive multimedia content when it receivesa request to do so (e.g., as in step 402). The request may be submitted,for example, by an operator of the vehicle, a seller or lessor of thevehicle, and/or an employee of an auto dealership. The request may beinputted into a device of the server, e.g., via update interface 372 ofserver system 350. In some aspects, the request may be automatic and maybe received when a staging area system detects a vehicle (e.g., viavehicle detector 332 of the staging area system 320). At step 404, aserver may receive vehicle-specific identifying information of avehicle. In some aspects, the server may prompt the sender of therequest in step 402 (e.g., an operator of the vehicle) for thevehicle-specific identifying information. Also or alternatively, therequest received at step 402 may identify the vehicle for which theinteractive multimedia content with the annotations is being sought. Theidentification of the vehicle may include information that the servermay use to look up the vehicle profile of the vehicle, e.g., via thevehicle profile database 358 of the server system 350. Also oralternatively, generated multimedia content of the vehicle received insubsequent steps of method 400 may be used to automatically recognizevehicle-specific identifying information and/or the vehicle profile ofthe vehicle.

In some aspects, the server may use the vehicle-specific identifyinginformation to retrieve accident reports of the vehicle (e.g., as instep 405). The accident reports may be retrieved from external systems(e.g., the external systems 380), such as the computing systems ofmunicipal offices, police departments, or governmental agencies.Accident reports and other data pertaining to a vehicle may be saved atthe server, e.g., within the vehicle profiles database 358 and indexedunder a data structure assigned for the vehicle.

At step 406, the server may prompt and/or authorize a staging areasystem to generate multimedia content showing one or more aspects of thevehicle. For example, if the request to generate the interactivemultimedia content and the annotations was automatically sent by thestaging area system, the prompting in step 406 may allow the stagingarea system to send multimedia content to the server. An authorizationmay be an authentication of the vehicle as it is detected by the stagingarea system (e.g., via vehicle detector 332 of the staging area system320). For example, the detected vehicle may be compared with thevehicle-specific identifying information received by the server toensure that the vehicle that has entered the staging area system is thecorrect vehicle (e.g., the vehicle for which the request for interactivemultimedia content with annotations to indicate instances of damage hadbeen sent). As will be described in relation to method 500 shown in FIG.5 , the staging area system may cause its instruments to generatemultimedia content following this authorization by the server.

At step 408, the server may receive the generated multimedia contentshowing the one or more aspects of the vehicle. For example, thegenerated multimedia content may be received from the staging areasystem 320 over communications network 380, and may be saved in thedatabases 352 (e.g., within multimedia content repository 356). Thelinking engine 366 may associate each multimedia content file with theaspect of the vehicle it represents, as well as the vehicle profile ofthe vehicle it represents. The aspect represented by the multimediacontent, and the vehicle profile of the vehicle associated with themultimedia content may be stored with the multimedia content, e.g., asmetadata. As discussed previously, an aspect of a vehicle may refer toone or more of: an exterior or interior region, an electrical and/ormechanical component of the vehicle, or a performance of the vehicle. Anaspect of a vehicle may be susceptible to damage (e.g., dents, leakages,wear and tear, etc.). A condition or state of an aspect of a vehicle maybe ascertained through a multimedia content (e.g., image, audio, video,etc.) capturing physical data pertaining to the aspect. The generatedmultimedia content received from the staging area system may be for amultitude of aspects of the vehicle so as to ensure that the interactivemultimedia content generated by the server shows annotations for damagesto any of the aspects of the vehicle. Thus, an example of the generatedmultimedia content received by the server may include images of thefront and rear exterior, side exterior, the top exterior, and the seats;an audio of the sound of the engine while the accelerator is pressed;and a video of the bottom of the vehicle, e.g., to capture any leaks influid.

At step 410, the server may extract data from the multimedia content(e.g., image data, video data, sound data, etc.), corresponding to eachof the one or more aspects of the vehicle. For example, if the receivedmultimedia content are images of the exterior of the vehicle, the servermay crop out parts of the image that are of the background, partitionthe cropped image into the various portions of the exterior of thevehicle (e.g., front exterior, rear exterior, etc.) that correspond toone or more aspects of the vehicle, and obtain image data (e.g., pixels,base code, etc.) for each of the portions. The one or more aspects ofthe vehicle may be identified through mathematical models that aretrained to recognize data pertaining to the one or more aspects of thevehicle. For example, as shown in step 409, the extraction of the datamay be performed by identifying trained models for aspect identificationbased on the vehicle profile. The trained models may be specific to avehicle profile, and may be stored in the server (e.g., as trainedmodels for aspect identification 360 within the vehicle profile database358 of server system 350). The vehicle profile of the vehicle may bedetermined from the vehicle-specific identifying information received instep 404. The training and application of these models for identifyingone or more aspects from the received multimedia content, e.g., toextract data corresponding to the one or more aspects will be explainedin further detail in relation to FIG. 7 .

For each of the one or more aspects of the vehicle, the server may inputits corresponding extracted data into a trained classification model toidentify any instances of damage (e.g., as in step 412). The trainedclassification models may be machine learning algorithms trained torecognize damages from a data (e.g., image data) pertaining to an aspectof a vehicle. The training may involve learning from training datacomprising reference data of an aspect of a vehicle showing one level ofdamage and reference data of the aspect of the vehicle showing anotherlevel of damage (e.g., no damage). The trained classification models maybe specific to a vehicle profile, and/or may be specific to an aspect ofa vehicle of a vehicle profile. Thus, the trained classification modelsmay be stored in the server (e.g., as trained models for damagedetection 362 stored within the vehicle profiles database 358 of serversystem 350). For example, a trained classification model for the frontexterior of vehicles belonging to the vehicle profile of a 2019 TOYOTACOROLLA LE may recognize any instances of damage to front exterior of avehicle that belongs to 2019 TOYOTA COROLLA LE vehicle profile. Thus,for each of the one or more aspects of the vehicle, the server may inputthe extracted data from the multimedia content corresponding to theaspect of the vehicle into the trained classification modelcorresponding to the aspect of the vehicle. More examples of thetraining and application of the classification models for identifyingany instances of damage to an aspect of a vehicle may be explained infurther detail in relation to FIG. 6 .

For each of the one or more aspects of the vehicle, the trainedclassification model corresponding to the aspect of the vehicle maydetermine whether or not the aspect of the vehicle whose condition wascaptured by the multimedia content has any instance of damage (e.g., asin step 414). If there are no instances of damage, the server maydetermine whether the extracted data of other aspects of the vehicle areyet to be examined, e.g., are yet to be used in determining whetherthere are any instances of damage to the aspect of the vehicle that theextracted data represents. If there are extracted data of other aspectsof the vehicle that are yet to be examined, step 412 may be repeated,e.g., by inputting the extracted data for each of the other aspects ofthe vehicle into the trained classification model corresponding to theother aspect of the vehicle.

If the server, via the trained classification model, determines that anaspect of the vehicle has an instance of damage, the server may addannotations to indicate the instance of damage to the aspect of thevehicle. The added annotations may be data added to the extracted data(e.g., making enhancements to the image data) that would cause theresulting data to generate a multimedia content of the aspect of thevehicle with an annotation indicating the damage (e.g., an image of theaspect that is annotated to indicate information pertaining to a damageto the aspect). The annotation may include, for example, one or more ofa label, an arrow, a link, a text (including a mouse over text), avisual display or symbol, etc. For example, if the data corresponding toan image of a front exterior of a vehicle showing a dent is base codethat compiles to generate the image, the server may update the base codeso that the resulting image generated by the updated base code shows anarrow pointing to the dent and a text indicating “dent.”

In some aspects, metadata received along with the generated multimediacontent in step 408 may be used in the annotation. For example, theannotation may indicate the time of damage, the first detection of thedamage (e.g., when the timing of the generation of the multimediacontent that captured the aspect of the vehicle showing the damage), thecause of the damage, any repair or maintenance associated with thedamage, etc.

In some aspects, e.g., where the server has an accident report of thevehicle, the server may associate the instance of damage (detected instep 414) to accident-specific data from the accident report. Forexample, if the server has detected damage to the front exterior to thevehicle based on image data of the front exterior indicating an instanceof damage, the server may electronically process the accident report(e.g., via word recognition for “front,” and/or “exterior”) to obtainaccident-specific data. The accident-specific data may indicate, forexample, a cause or source of the damage, a timing of the damage, alocation of the damage, cost of the damage, repairs or maintenanceassociated with the damage, etc. The accident-specific data may be usedin the annotation added to the data of the multimedia content for theaspect of the vehicle having the damage.

After the server has added the annotation to the data of the multimediacontent for the given aspect of the vehicle, the server may, at step418, determine whether the extracted data of other aspects of thevehicle are yet to be examined, e.g., are yet to be used in the trainedclassification models to determine whether there are any instances ofdamage to the aspect of the vehicle that the extracted data represents.If there are extracted data of other aspects of the vehicle that are yetto be examined, steps 412, 414, 416, and/or 417 may be repeated untilall or at least a predetermined number of aspects of the vehicle havebeen examined for damages.

If the extracted data of all or at least a predetermined number ofaspects of the vehicle have been examined for damages (e.g., step418=Yes), then the server may, at step 420, generate an interactivemultimedia content of the vehicle. The interactive multimedia contentmay display multimedia content for one or more aspects of the vehicle(which may overlap with the one or more aspects for which generatedmultimedia content was received in step 408). Furthermore, theinteractive multimedia content may include the annotations (as added instep 416) to indicate instances of damage to the one or more aspects ofthe vehicle. The interactive multimedia content and/or the annotationsmay be accessible to a user (e.g., user 202) of the online marketplaceof vehicles for sale, rent, or other leasing arrangements. The user mayaccess the interactive multimedia content of the vehicle through his orher user device (e.g., device 109, user device 202, and/or user device302). For example, while the user is browsing vehicles on the onlinemarketplace on his or her user device, the user may desire to know moreabout one of the vehicles listed in the online marketplace, and mayaccess the interactive multimedia content to learn more. The user maydiscover, by viewing and interacting with the interactive multimediacontent associated with the vehicle, of damages to one or more aspectsof the vehicle. Furthermore, the user may learn more about the damagesby reading and/or viewing the annotations. The user may be able toadjust the interactive multimedia content (e.g., by zooming, rotating,etc.) and may be able to shift through details and annotations for eachof the one or more aspects of the vehicle using a user interface (e.g.,user interface 312 of the user device 302). In some aspects, where theserver manages applications for presenting the interactive multimediacontent of vehicles and/or manages the online marketplace, the servermay update the application by deploying the interactive multimediacontent (e.g., via the API 368 of the server system 350).

FIG. 5 depicts a flow diagram of an example method 500 for facilitatingautomatic annotation for vehicle damage, in accordance with one or moreillustrative aspects discussed herein. Method 500 may be performed byone or more components (e.g., processors) of the staging area system 320shown in FIG. 3 , staging area system 218 shown in FIG. 2 , and/ordevice 105 shown in FIG. 1 . For convenience, “staging area system” maybe used herein to identify the performer of one or more steps of method500. Furthermore the staging area system may be associated with astaging area (e.g., staging area 212 as shown in FIG. 2 ) and may sendand receive communications with the server in method 400 (e.g., serversystem 350 shown in FIG. 3 , server 201 shown in FIG. 2 , and/orcomputing device 101 shown in FIG. 1 ). Accordingly, “staging area” and“server” may be used when describing one or more steps of method 500,for convenience. As explained previously, a vehicle may be examined forinstances of damage when the vehicle is brought to a staging area.Instruments, devices, and/or systems of the staging area system mayproceed to generate and send multimedia content, according to one ormore steps described herein. Furthermore, one or more steps of method500 may be performed by the staging area system in parallel to,subsequent to, or prior to one or more steps of method 400 performed bythe server.

At step 502, the staging area system may detect a vehicle entering, orsituated in, the staging area (e.g., as in staging area 212 associatedwith the staging area system 218). The detection may be by way of avehicle detector (e.g., vehicle detector 214 as shown in FIG. 2 andvehicle detector 332 as shown in FIG. 3 ). The vehicle detector may be amotion detector, heat sensor, and/or an image sensor that is able tosense the presence or entry of a vehicle. Through this detection, thestaging area system may deliver a signal to the server (e.g., that avehicle has entered the staging area). In addition to or as analternative to the detection of the vehicle, a signal may be sent to theserver (e.g., manually by an operator of the vehicle) from the stagingarea system. In some aspects, the signal may be a request for aninteractive multimedia content for the vehicle and/or a request togenerate annotations indicating damages to one or more aspects of thevehicle, as previously discussed, with respect to step 402 of method400.

At step 503, the staging area system may send an indication of thedetection and/or any vehicle-specific identifying information of thevehicle. Also or alternatively, the signal sent at 502 may include thevehicle-specific identifying information of the vehicle. Furthermore,the vehicle-specific identifying information may be manually enteredinto the staging area system (e.g., via an input module at the stagingarea system). The vehicle-specific identifying information may includefor example, one or more of, a vehicle identification number, a make ormanufacturer of the vehicle, the class of the vehicle, a vehicle type, ayear of manufacture of the vehicle, a color or other physicalcharacteristic of the vehicle, a nameplate identification of thevehicle, and/or a driver license number associated with the vehicle.Also or alternatively, instruments associated with the staging area(e.g., the instruments 224 and 228) can generate multimedia content(e.g., photos, videos, sound recordings, etc.) of the incoming or placedvehicle and the multimedia content can be used to not only capture thecondition of one or more aspects of the vehicle but also to initiallydetect the vehicle.

The staging area system may attempt to authenticate to confirm whetherthe vehicle detected in step 502 is a vehicle for which the interactivemultimedia content and annotations has been requested from the server.For example, the server may attempt to match vehicle-specificidentifying information of the vehicle (e.g., by an operator of thevehicle, a seller or lessor of the vehicle, an employee of an autodealership, etc.) with additional information generated at the stagingarea system and sent to the server. The additional information may byrequested by the server and may include, for example, an image or scanof a vehicle identification (e.g., VIN) displayed on the vehicle, animage or scan of a nameplate identification of the vehicle, and/or animage or scan of a defining feature of the vehicle that could be used tomatch vehicle-specific identifying information (e.g., a displayed logoor name of the vehicle manufacture and/or class). The staging areasystem may comply with such requests for additional information.Subsequently, the staging area system may receive an indication that thevehicle has been authenticated (e.g., as in step 504).

After the vehicle has been authenticated, the server may proceed toprompt or authorize the staging area system to generate multimediacontent of one or more aspects of the vehicle (e.g., beginning with step506). At step 506, the staging area system may receive a command togenerate multimedia content showing the one or more aspects of thevehicle. An aspect of a vehicle, which may include, for example, anexterior or interior region, an electrical and/or mechanical componentof the vehicle, or a performance of the vehicle, may be susceptible todamage (e.g., dents, leakages, wear and tear, etc.). The staging areasystem may be communicatively coupled to various instruments (e.g.,instruments 224 and 228 as shown in FIG. 2 , and instruments 322 asshown in FIG. 3 ) that could generate multimedia content that capturesphysical data of the one or more aspects of the vehicle. Based on thereceived command, the staging area system may cause the instruments togenerate the multimedia content. For example, cameras located rightabove the vehicle may take photos of the top exterior region of thevehicle, while cameras located on the floor of the staging area may takevideos of the bottom of the vehicle (e.g., to capture any leakages fromthe vehicle).

At step 510, the staging area system may then send the multimediacontent to the server. Furthermore, the time of generation of themultimedia content and/or any other detail associated with thegeneration of the multimedia content (e.g., the instrumentidentification, location of the staging area, location of theinstrument, aspect captured, etc.) may also be sent with the multimediacontent to the server. As described previously in relation to method 400of FIG. 4 , the server may receive the generated multimedia content(along with the time of generation and/or any other metadata) todetermine any instances of damages to the one or more aspects of thevehicle, and may generate interactive multimedia content of the vehiclewith annotations to indicate the instances of damage.

FIG. 6 depicts a flow diagram of example methods for training aclassification model (e.g., method 600A) and applying the classificationmodel (e.g., method 600B) to determine one or more instances of damageto a vehicle, in accordance with one or more illustrative aspectsdiscussed herein. Furthermore, FIG. 6 describes an example of one ormore steps that may be performed prior to, in parallel to, or subsequentto step 412 of method 400, as shown in FIG. 4 . Methods 600A (e.g., thetraining phase of the classification model) and 600B (e.g., theapplication phase of the classification model) may be performed by theserver of method 400 (e.g., server system 350 shown in FIG. 3 , server201 shown in FIG. 2 , and/or computing device 101 shown in FIG. 1 ). Insome aspects, however, method 600A may be performed by an externalsystem, and the resulting trained classification model may be providedto the server for use in method 600B. For convenience, “server” may beused herein to identify the performer of one or more steps of method600B (e.g., the application phase), and “device” (which may or may notnecessarily be the server) may be used herein to identify the performerof one or more steps of method 600A (e.g., the training phase). Whileimage data is being used in methods 600A and 600B, it is contemplatedthat other forms of multimedia content data (e.g., audio data, videodata, etc.) may additionally be used and/or may be substituted for theimage data.

Method 600A may describe example steps for training classificationmodels that are able to identify an instance of damage from multimediacontent data corresponding to one or more aspects of a vehicle. Method600A may rely on training data associated with a plurality of vehiclesthat may be different from the vehicle for which an interactivemultimedia content and annotations are generated in method 400. Theabove described plurality of vehicles associated with the training datamay be referred to as “reference vehicles,” and may belong to the samevehicle profile, or share at least some vehicle-specific identifyinginformation (e.g., same manufacturer, same class, same year ofmanufacture, etc.) as the vehicle for which the interactive multimediacontent and annotations is being requested. The training data may beobtained, e.g., from external systems (e.g., computing systems ofvehicle manufacturer and/or dealerships). Also or alternatively, thetraining data (e.g., multimedia content of one or more aspects of thereference vehicles) may be periodically obtained and saved, e.g., in themultimedia content repository 356 of server system 350. As will bedescribed further, each of the trained classification models may bespecific to an aspect of a vehicle and/or vehicle profile. For example,each trained classification model may be able to identify an instance ofdamage to an exterior or interior region, an electrical and/ormechanical component of the vehicle, and/or a performance of thevehicle, based on an input of multimedia content data (e.g., image data)corresponding to the exterior or interior region, the electrical and/ormechanical component of the vehicle, and/or the performance of thevehicle.

Thus, at step 602, the device may receive, for each of a plurality ofaspects for a reference vehicle, at least, (1) a first set of imagesshowing a first level of damage to an aspect of the reference vehicle,and a (2) a second set of images showing a second level of damage to theaspect of the reference vehicles. The “reference vehicle,” although usedsingularly, may refer to more than one vehicle that belongs to aspecific vehicle profile or that share at least some vehicle-specificidentifying information as the vehicle for which the interactivemultimedia content and annotations is requested. A level of damage mayrefer to a degree of damage and may include an absence of any damage(e.g., no damage). The damage may include, for example, a deterioration,a dent, a rupture, a decrease in performance, an unusual sound oractivity, a malfunction, etc. The first set of images and second set ofimages may be saved, e.g., in the multimedia content repository of 356of server system 350 and may be labeled or marked accordingly (e.g., toindicate the aspect it represents and/or the vehicle profile of thereference vehicle associated with the image).

Furthermore, the device may associate image data from the first set ofimages with the first level of damage and may associate image data fromthe second set of images with the second level of damage (e.g., as instep 604). The association may be a mapping or linkage between a domain(e.g., image data) and a range (e.g., level of damage) to further theformation of a mathematical relation for machine learning purposes. Thefirst set of images and second set of images may be processed by thedevice to create their respective image data.

At step 606, (e.g., for each of the plurality of aspects of thereference vehicles) the device may train a classification model for thereference vehicle using the associated image data. The training mayinvolve determining a mathematical relation between the domain (e.g.,image data) and the range (e.g., level of damage) of the training data.Furthermore, the training may involve classifications of a type of imagedata that lead to a finding of an instance of damage from another typeof image data that lead to a finding of no damage. However, the types ofimage data may not necessarily have to correspond with the image data ofthe first set of images and the image data of the second set of images,respectively. Furthermore, more than two sets of images and theirrespective image data may be used in the training phase, for example, tolearn the identification of a greater variability in the severity ofdamage to an aspect of a vehicle. Examples of classification models thatmay be used in the training may include, but are not limited to,k-nearest neighbor algorithms, case-based reasoning, decision trees,Bayes classifiers, and artificial neural networks. Each of the trainedclassification model may be used to identify an instance of damage to anaspect of a vehicle belonging to a vehicle profile, from image data ofan image of the aspect of the vehicle. Thus a trained classificationmodel may be created for each of the one or more aspects of the vehiclefrom the vehicle profile. In some aspects, the trained classificationmodel for one aspect of vehicles belonging to one vehicle profile may beapplicable to the same aspect of another vehicles belonging to anothervehicle profile. For example, some aspects of a vehicle, such as anengine performance, may be similar or identical across various vehicleprofiles (e.g., an engine from a 2019 TOYOTA COROLLA may be the same asthe engine from a 2019 TOYOTA CAMRY). At step 608, the trainedclassification models may thus be stored, e.g., in the vehicle profilesdatabase 358 of the server system 350. In aspects where the training ofthe classification models (e.g., steps 602 through 606 of method 600A)occurs at an external system, the server may receive the trainedclassification and store them accordingly. The trained classificationmodels may be indexed and/or labeled based on the aspect of thereference vehicle associated with the trained classification models, andbased on the vehicle profile of the reference. The indexing and/orlabeling (e.g., during storage) may ensure efficient identification andretrieval of the trained classification model in method 600B, as will bediscussed below.

As discussed previously, method 600B may describe example steps forapplying the training classification model to identify an instance ofdamage from multimedia content data (e.g., image data) corresponding toone or more aspects of a vehicle. The vehicle, for which the instancesof damage to one or more of its aspects will be identified, may be thevehicle for which interactive multimedia content and annotations arerequested (e.g., as in the vehicle 206 shown in FIG. 2 .

At step 610, the server may determine a vehicle profile for the vehiclefor which the interactive multimedia content and annotations isrequested. As discussed previously in relation to step 404 shown in FIG.4 , the server may receive vehicle-specific identifying information ofthe vehicle. The vehicle-specific identifying information may providesufficient information for the server to determine a vehicle profile ofthe vehicle (e.g., as in step 610). The determined vehicle profile mayassist the server in identifying and retrieving the relevant trainedclassification model in subsequent steps.

At step 612, and as discussed previously in relation to step 408 shownin FIG. 4 , the server may receive images showing one or more aspects ofthe vehicle. Image data (or other computer readable media) may beextracted from the images (e.g., as discussed previously in relation tostep 410 show in FIG. 4 ) to be input into the trained classificationmodels.

At step 614, the server may identify and retrieve, based on the vehicleprofile of the vehicle, one or more trained classification models forthe one or more aspects of the vehicle. The one or more trainedclassification models may have been trained using method 600A and stored(e.g., from step 608) based on vehicle profile information. Thevehicle-specific identifying information of the vehicle for whichinteractive multimedia content is requested may be used to determine thevehicle profile of the vehicle. As discussed previously, classificationmodels may be trained (e.g., via method 600A) to identify instances ofdamage to a specific aspect of a vehicle belonging to a vehicle profile.Since the trained classification models may be specific to an aspect ofvehicles belonging to the vehicle profile, the server may use thedetermined vehicle profile of the vehicle to identify and retrieve atrained classification model for each of the one or more aspects of thevehicle profile of the vehicle.

At step 616, for each of the one or more aspects of the vehicle, theserver may input image data of the received images into the trainedclassification model corresponding to each aspect. As was discussedpreviously in relation to method 600A, the classification models may betrained so that an input of image data of an image corresponding to anaspect of a vehicle would output instances of damage.

Thus, at step 618, the server may determine any one or more instances ofdamage to the vehicle using the trained classification models. Forexample, the server may aggregate the instances of damage to each of theone or more aspects of the vehicle to determine the instances of damageto the vehicle as a whole. As discussed previously, in relation tomethod 400 shown in FIG. 4 , the server may annotate the instances ofdamage with relevant information and generate an interactive multimediacontent of the vehicle.

FIG. 7 depicts a flow diagram of example methods for training a machinelearning model (e.g., method 700A) and applying the machine learningmodel (e.g., method 700B) to identify one or more aspects of a vehicle,in accordance with one or more illustrative aspects discussed herein.Thus, while classification models described in relation to FIG. 6 may beused to determine instances of damage to one or more aspects of avehicle, the machine learning models described in relation to FIG. 7 maybe used to identify one or more aspects of a vehicle, e.g., from imagesof a vehicle. For example, method 700B may be used by the server toidentify, from an image of a region of a vehicle received from thestaging area system, that the received image is of a front exteriorregion of the vehicle, while method 600B may be used by the server todetermine any instances of damage to the front exterior region of thevehicle. Furthermore, FIG. 7 describes an example of one or more stepsthat may be performed prior to, in parallel to, or subsequent to steps409 and 410 of method 400, as shown in FIG. 4 . Methods 700A (e.g., thetraining phase of the machine learning model) and 700B (e.g., theapplication phase of the machine learning model) may be performed by theserver of method 400 (e.g., server system 350 shown in FIG. 3 , server201 shown in FIG. 2 , and/or computing device 101 shown in FIG. 1 ). Insome aspects, however, method 700A (e.g., the training phase) may beperformed by an external system, and the resulting trained machinelearning model may be provided to the server for use in method 700B. Forconvenience, “server” may be used herein to identify the performer ofone or more steps of method 700B (e.g., the application phase), and“device” (which may or may not necessarily be the server) may be usedherein to identify the performer of one or more steps of method 700A(e.g., the training phase). While image data is being used in methods700A and 700B, it is contemplated that other forms of multimedia contentdata (e.g., audio data, video data, etc.) may additionally be usedand/or may be substituted for the image data.

Method 700A may describe example steps for training machine learningmodels that are able to identify an aspect of a vehicle (e.g., anexterior or interior region of a vehicle) from image data of an image ofthe aspect of the vehicle. Based on the identification, the image and/orimage data may be labeled or indexed as pertaining to the identifiedaspect. Method 700A may rely on training data associated with aplurality of vehicles that may be different from the vehicle for whichan interactive multimedia content and annotations are generated inmethod 400. The above described plurality of vehicles associated withthe training data may be referred to as “reference vehicles,” and maybelong to the same vehicle profile, or share at least somecharacteristics (e.g., same manufacturer, same class, same year ofmanufacture, etc.) as the vehicle for which the interactive multimediacontent and annotations is being requested. The training data may beobtained, e.g., from external systems (e.g., computing systems ofvehicle manufacturer and/or dealerships). Also or alternatively, thetraining data (e.g., multimedia content of one or more aspects of thereference vehicles) may be periodically obtained and saved, e.g., in themultimedia content repository 356 of server system 350. As will bedescribed further, each of the trained machine learning models may bespecific to a vehicle profile. For example, each trained machinelearning model may be able to input image data of an image of a part ofthe vehicle (e.g. a front exterior region of the vehicle) and output arecognition or identification of the image as displaying a specificaspect of the vehicle (the front exterior region of the vehicle).

Referring to method 700A, the device may receive, for each of aplurality of reference vehicles, various sets of images, wherein eachset of images may show an aspect of the reference vehicle. For example,the device may receive (1) a first set of images showing a first aspectof the reference vehicle, (2) a second set of images showing a secondaspect of the reference vehicle, . . . and (n) an nth set of imagesshowing an nth aspect of the reference vehicle. The plurality ofreference vehicles may belong to a vehicle profile, as different vehicleprofiles may yield different characteristics for the one or more aspects(e.g., a TOYOTA COROLLA SEDAN may have a different shape than a TESLASUV). Since the plurality of reference vehicles may belong to a specificvehicle profile, the resulting trained machine learning model in method600A may be specific to a vehicle profile. Nevertheless, in someaspects, the trained machine learning models need not be specific to avehicle profile. For example, some aspects of a vehicle, such as theinterior back seats, may be similar or identical across various vehicleprofiles (e.g., the interior back seats of a 2019 TOYOTA COROLLA mayappear to be the same as the interior back seats of a 2019 HONDAACCORD).

For each of the plurality of reference vehicles, the device mayassociate image data of each of the various sets of images with theaspect that the set of images represents (e.g., as in step 704). Thus,the device may associate, for each of the plurality of referencevehicles, (1) image data from the first set of images with the firstaspect, (2) image data from the second set of images with the secondaspect, . . . and (n) image data from the nth set of images with the nthaspect. The association may be a mapping or linkage between a domain(e.g., image data) and a range (e.g., an identification of the aspectrepresented by the image of the image data) to further the formation ofa mathematical relation for machine learning purposes. The sets ofimages may be processed by the device to create their respective imagedata.

At step 706, the device may train machine learning models to identifythe one or more aspects of the plurality of reference vehicles, usingimage data of images representing the one or more aspects. The trainingmay be by learned recognition of various features of an image data thatsignify or are emblematic of the aspect represented. The training mayinvolve determining a mathematical relationship between the domain(e.g., image data) and the range (e.g., an identification of the aspectrepresented by the image of the image data) of the training data.

At step 708, the device may store the trained machine learning modelsfor retrieval, e.g., in method 700B. The trained machine learning modelsmay be stored in the server (e.g., as the trained models for aspectidentification 360 in the vehicle profiles database 358 of the serversystem 350). The trained machine learning models may be indexed and/orlabeled based on the vehicle profile of the reference vehicles. Theindexing and/or labeling (e.g., during storage) may ensure efficientidentification and retrieval of the trained classification model inmethod 600B, as will be discussed below.

As discussed previously, method 700B may describe example steps forapplying the training machine learning models to recognize and/oridentify one or more aspects of a vehicle in a multimedia contentrepresenting the one or more aspects of a vehicle from multimediacontent data (e.g., image data) of the multimedia content. The vehicle,for which the instances of damage to one or more of its aspects will beidentified, may be the vehicle for which interactive multimedia contentand annotations are requested (e.g., as in the vehicle 206 shown in FIG.2 .

At step 710, the server may determine a vehicle profile for the vehiclefor which the interactive multimedia content and annotations isrequested. As discussed previously in relation to step 404 shown in FIG.4 , the server may receive vehicle-specific identifying information ofthe vehicle. The vehicle-specific identifying information may providesufficient information for the server to determine a vehicle profile ofthe vehicle (e.g., as in step 710). The determined vehicle profile mayassist the server in identifying and retrieving the relevant trainedmachine learning model in subsequent steps.

At step 712, and as discussed previously in relation to step 408 shownin FIG. 4 , the server may receive images showing one or more aspects ofthe vehicle. Image data (or other computer readable media) may beextracted from the images (e.g., as discussed previously in relation tostep 410 show in FIG. 4 ) to be input into the trained machine learningmodels.

At step 714, the server may identify and retrieve, based on the vehicleprofile of the vehicle, a trained machine learning model associated withthe vehicle profile of the vehicle. As discussed previously, machinelearning models may be trained (e.g., via method 700A) to identifyand/or recognize aspects of a vehicle (e.g., interior and exteriorregions of a vehicle) from image data of images of the vehicle.

At step 716, the server may input image data of the received images intothe trained machine learning model. As was discussed previously inrelation to method 600A, the machine learning models were trained sothat an input of image data of an image corresponding to an aspect of avehicle would output an identification of the aspect (e.g., “this imagerepresents the front exterior of the 2019 TOYOTA COROLLA LE”).

Thus, at step 718, the server may determine aspects represented in theimages receive in step 712 (and step 408 as shown in FIG. 4 ), via thetrained machine learning model. The identification of aspectsrepresented by the images may be useful in subsequent steps of method400, as shown in FIG. 4 . For example, the server may use the identifiedaspects to identify and retrieve trained classification models to detectany instances of damage to the each of the identified aspects (e.g.,using method 600B discussed previously).

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A method comprising: receiving vehicle-specificidentifying information corresponding to a vehicle; determining imagedata corresponding to one or more aspects of the vehicle; determining,using a trained classification model, one or more instances of damage tothe one or more aspects; annotating, based on the one or more instancesof damage, the image data corresponding to the one or more aspects toreflect an indication of the one or more instances of damage; receivingan input comprising further vehicle-specific information associated withthe one or more instances of damage; generating an interactivemultimedia content associated with the vehicle, wherein the interactivemultimedia content comprises the annotated image data indicating the oneor more instances of damage and the further vehicle-specific informationassociated with the one or more instances of damage; and causing,through a user interface and responsive to a request, display of theinteractive multimedia content of the vehicle.
 2. The method of claim 1,further comprising: prior to the determining the one or more instancesof damage, receiving, for each of a plurality of exterior regions of aplurality of reference vehicles, a first set of images of an aspectshowing no instances of damage and a second set of images of the aspectshowing one or more reference instances of damage; training theclassification model using the first set of images and the second set ofimages for each of one or more aspects of the reference vehicles,wherein the classification model is trained to detect one or moreinstances of damage on an aspect of the reference vehicle; and storingthe trained classification model.
 3. The method of claim 1, wherein thedetermining the image data corresponding to the one or more aspects ofthe vehicle further comprises: inputting generated multimedia contentinto a trained machine learning model to detect the one or more aspectsof the vehicle.
 4. The method of claim 3, further comprising: prior tothe inputting the generated multimedia content into the trained machinelearning model, receiving, for each of one or more aspects of aplurality of reference vehicles, training data comprising: multimediacontent showing the one or more aspects of the plurality of referencevehicles, and an identification of image data of the multimedia contentcorresponding to the one or more aspects of the plurality of referencevehicles; training a machine learning model by learning recognition ofthe image data corresponding to each of the one or more aspects from thereceived multimedia content; and storing the trained machine learningmodel.
 5. The method of claim 1, further comprising: receiving, asmetadata of the image data, a time of generation of the image data; andannotating, based on the time of generation, the image data to furtherindicate a time of detecting the one or more instances of damage.
 6. Themethod of claim 1, further comprising: prompting the input comprisingthe further vehicle-specific information associated with the one or moreinstances of damage; and receiving, in response to the prompting, theinput comprising the further vehicle-specific information associatedwith the one or more instances of damage.
 7. The method of claim 1,wherein the receiving the input comprising further vehicle-specificinformation comprises: receiving, from an external device, an accidentreport of the vehicle; and associating, to the one or more instances ofdamage, accident-specific data from the accident report.
 8. A systemcomprising: a first device comprising: one or more first processors; anda first memory storing first instructions that, when executed by the oneor more first processors, cause the first device to: receivevehicle-specific identifying information corresponding to a vehicle;determine image data corresponding to one or more aspects of thevehicle; determine, using a trained classification model, one or moreinstances of damage to the one or more aspects; annotate, based on theone or more instances of damage, the image data corresponding to the oneor more aspects to indicate the one or more instances of damage;receive, from an external device, an input comprising furthervehicle-specific information associated with the one or more instancesof damage; and generate an interactive multimedia content associatedwith the vehicle, wherein the interactive multimedia content comprisesthe annotated image data indicating the one or more instances of damageand the further vehicle-specific information associated with the one ormore instances of damage; and a second device comprising: one or moreimage sensors; one or more second processors; and a second memorystoring second instructions that, when executed by the one or moresecond processors, cause the second device to: detect, using the one ormore image sensors, that the vehicle is entering a predetermined areaassociated with the second device; generate, using the one or more imagesensors, the image data; and send the image data to the first device. 9.The system of claim 8, wherein the first instructions, when executed bythe one or more first processors, cause the first device to: prior tothe determining the one or more instances of damage, receive, for eachof a plurality of aspects of reference vehicles, a first set of imagesand a second set of images, wherein the first set of images shows afirst level of damage to an aspect of the reference vehicle, and whereinthe second set of images shows a second level of damage to the aspect ofthe reference vehicle; train, using the first set of images and thesecond set of images, and based on detecting the first level of damageand the second level of damage, the classification model; and store thetrained classification model in the first memory.
 10. The system ofclaim 9, wherein the first instructions, when executed by the one ormore first processors, cause the first device to: prior to thedetermining the one or more instances of damage, identify, based on thevehicle-specific identifying information of the vehicle, a trainedclassification model from a plurality of trained classification modelsstored in the first memory.
 11. The system of claim 8, wherein the firstinstructions, when executed by the one or more first processors, causethe first device to determine the image data by: inputting, into atrained machine learning model, received multimedia content to detectone or more exterior regions of the vehicle.
 12. The system of claim 11,wherein the first instructions, when executed by the one or more firstprocessors, cause the first device to: prior to the inputting thereceived multimedia content into the trained machine learning model,receive, for each of the one or more aspects of a plurality of referencevehicles, training data comprising: multimedia content showing the oneor more aspects of the plurality of reference vehicles, and anidentification of image data of the multimedia content corresponding tothe one or more aspects of the plurality of reference vehicles; train,by learned recognition of the image data corresponding to each of theone or more aspects from the multimedia content, a machine learningmodel; and store the trained machine learning model in the first memory.13. The system of claim 12, wherein each of the plurality of referencevehicles shares one or more vehicle-specific identifying informationwith the vehicle.
 14. The system of claim 8, wherein the firstinstructions, when executed by the one or more first processors, causethe first device to: receive, from the second device, a time ofgeneration of the image data, and annotate, based on the time ofgeneration, the image data to further indicate a time of detecting theone or more instances of damage.
 15. The system of claim 14, wherein thetime of generation is received as metadata of the image data.
 16. Thesystem of claim 8, wherein the first instructions, when executed by theone or more first processors, cause the first device to: prior to thereceiving the input, prompt the input comprising the furthervehicle-specific information associated with the one or more instancesof damage, wherein the receiving the input is responsive to theprompting.
 17. The system of claim 8, wherein the first instructions,when executed by the one or more first processors, cause the firstdevice to receive the input by: receiving, from an external device, anaccident report of the vehicle; and associating, to the one or moreinstances of damage, accident-specific data from the accident report.18. The system of claim 8, wherein the first instructions, when executedby the one or more first processors, cause the first device to: send, toa third device, the interactive multimedia content of the vehicle; andwherein the system further comprises: the third device comprising: auser interface; a display screen; one or more third processors; and athird memory storing second instructions that, when executed by the oneor more third processors, cause the third device to: receive, from thefirst device, the interactive multimedia content of the vehicle receivea request to view the interactive multimedia content of the vehicle;display the interactive multimedia content of the vehicle on the displayscreen; and enable, through the user interface, an interaction with theinteractive multimedia content to display the one or more aspects of thevehicle.
 19. One or more non-transitory storage media storinginstructions that, when executed by one or more processors, cause theone or more processors to perform steps comprising: a training phasecomprising: receiving, for one or more aspects of a plurality ofreference vehicles, a first training data comprising: image data, and anidentification of the image data; and training, using learnedrecognition of each aspect, and based on the image data, a machinelearning model; storing the trained machine learning model for anapplication phase; receiving, for each of a plurality of exteriorregions of the plurality of reference vehicles, a second training datacomprising: a first set of images showing a first level of damage to theaspect of the reference vehicles, and a second set of images showing asecond level of damage to the aspect of the reference vehicles; traininga classification model using the first set of images and the second setof images for each of the one or more aspects of the reference vehicles,wherein the classification model is trained to detect one or moreinstances of damage on an aspect of a vehicle different from thereference vehicles; and storing the trained classification model for theapplication phase; and the application phase comprising: receivingvehicle-specific identifying information of the vehicle; inputtingvehicle-specific identifying information into the trained machinelearning model to detect the one or more aspects of the vehicle.determining image data corresponding to the one or more aspects of thevehicle; determining, using the trained classification model, one ormore instances of damage to the one or more aspects; annotating, basedon the determined one or more instances of damage, the image datacorresponding to the aspect to indicate the one or more instances ofdamage; receiving an input comprising further vehicle-specificinformation associated with the one or more instances of damage;generating an interactive multimedia content associated with thevehicle, wherein the interactive multimedia content comprises theannotated image data indicating the one or more instances of damage andthe further vehicle-specific information associated with the one or moreinstances of damage; and sending, to a device for display and responsiveto a request, the interactive multimedia content of the vehicle.
 20. Theone or more non-transitory storage media of claim 19, storinginstructions that, when executed by the one or more processors, causethe one or more processors to perform steps further comprising:receiving, as metadata of the image data, a time of generation of theimage data; and annotating, based on the time of generation, the imagedata to further indicate a time of detecting the one or more instancesof damage.